Digital & AI Solutions
AI-Driven Optimization for Solvent Extraction & Oil Refineries
Modern oilseed crushing and refining plants operate on thin margins. Small improvements in yield, energy efficiency, and reliability translate directly into significant profit gains. By combining artificial intelligence (AI) with decades of hands-on plant experience, we help owners and operators move from reactive control to predictive, optimized operations.
Where AI Brings Real Value in Solvent Extraction & Refining
Extractor performance depends on multiple interacting variables—flake thickness, bed depth, miscella concentration, temperature, vacuum, solvent flow, and residence time. Most plants still rely heavily on operator experience and historical setpoints.
AI-based solution
We develop AI models trained on plant operating data, including:
- Flake thickness and feed rate
- Extractor and DT/DC temperatures
- Miscella concentration and solvent flow
- Vacuum stability
- Meal residual oil analysis
- AI outputs
- Predict residual oil in meal in real time
- Recommend optimal solvent rate and bed depth
- Early alarms when extraction efficiency begins to drift
- Business impact
- 0.2–0.4% improvement in oil recovery
- Direct, measurable profit increase per ton of seed processed
- AI predicts
- Required steam per ton of meal
- Final meal moisture and hexane residual
- Risk of over-steaming or under-desolventizing
- Key benefits
- 5–12% steam consumption reduction
- Improved meal quality (PDI, KOH solubility)
- Lower thermal damage and solvent losses
- Applicable equipment
- Extractor chains and drives
- Redler conveyors
- Pumps and vacuum systems
- Boilers, turbines, and utilities
- AI monitors
- Vibration trends
- Motor current and load profiles
- Temperature patterns
- Lubrication and maintenance history
- Results
- Predict failures before breakdowns occur
- Reduce unplanned downtime
- Optimize spare-parts inventory and maintenance planning
Applicable across:
- Degumming
- Neutralization
- Bleaching
- Deodorization
- AI predicts
- FFA reduction efficiency
- Final oil color
- Phosphorus levels after degumming
- Steam consumption in deodorization
- Consistent finished oil quality
- Reduced chemical losses
- Improved deodorizer energy efficiency
- AI continuously monitors
- Boiler efficiency and excess air
- Steam network balance
- Condensate recovery losses
- Cooling water and heat-exchange performance
- Impact
3–8% reduction in total energy costs - Improved utility system reliability
How This Matches Real Plant Experience
Unlike generic software providers, our AI solutions are built on deep operational understanding of solvent extraction plants and refineries.
Models are designed and validated using:
- Actual operating constraints
- Equipment-specific behavior
- Practical plant limits, not theoretical assumptions
- This ensures AI recommendations are implementable, trusted by operators, and commercially valuable.
Roles Required to Deliver These Solutions
A successful AI optimization program typically involves:
- Process Engineer / Domain Expert (solvent extraction & refining)
- Data Scientist / AI Engineer
- Automation & Instrumentation Specialist
- Maintenance & Reliability Engineer
- Our consulting model allows clients to access this expertise without building a full in-house team..
Practical Project Roadmap
Phase 1 – Assessment & Data Readiness
Review plant instrumentation, historians, and lab data
Identify high-impact optimization opportunities
Phase 2 – Model Development
Build AI models for yield, energy, and reliability
Validate predictions against historical performance
Phase 3 – Pilot Implementation
Deploy AI dashboards and alerts
Operator training and fine-tuning
Phase 4 – Scale & Continuous Improvement
Expand to additional units (refinery, utilities)
Business Model for AI-Based Consulting Services
We offer flexible engagement models:
- Project-based implementation
- Monthly optimization advisory retainer
- Performance-linked success fees (yield or energy savings)
- Remote monitoring & optimization support
